import numpy as np


def emailFeatures(word_indices):
    """takes in a word_indices vector and
    produces a feature vector from the word indices.
    """

# Total number of words in the dictionary
    n = 1899

# You need to return the following variables correctly.
    x = np.zeros(n)
# ====================== YOUR CODE HERE ======================
# Instructions: Fill in this function to return a feature vector for the
#               given email (word_indices). To help make it easier to 
#               process the emails, we have have already pre-processed each
#               email and converted each word in the email into an index in
#               a fixed dictionary (of 1899 words). The variable
#               word_indices contains the list of indices of the words
#               which occur in one email.
# 
#               Concretely, if an email has the text:
#
#                  The quick brown fox jumped over the lazy dog.
#
#               Then, the word_indices vector for this text might look 
#               like:
#               
#                   60  100   33   44   10     53  60  58   5
#
#               where, we have mapped each word onto a number, for example:
#
#                   the   -- 60
#                   quick -- 100
#                   ...
#
#              (note: the above numbers are just an example and are not the
#               actual mappings).
#
#              Your task is take one such word_indices vector and construct
#              a binary feature vector that indicates whether a particular
#              word occurs in the email. That is, x(i) = 1 when word i
#              is present in the email. Concretely, if the word 'the' (say,
#              index 60) appears in the email, then x(60) = 1. The feature
#              vector should look like:
#
#              x = [ 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 ... 0 0 0 1 0 ..]
#
#


# =========================================================================

    return x